With the increasing availability of three dimensional televisions, the demand for three dimensional content has also increased. Newly available three dimensional content does not fill the demand for the consumer market. Converting two dimensional (2D) content to three dimensional (3D) content can fill this growing need for high quality stereoscopic images. In addition, autostereoscopic displays are always in 3D mode, and the generation of 3D images from 2D content provides a more uniform 3D viewing experience.
Many methods exist for 2D-3D conversion. Many rely on depth maps. A depth map is an image that contains information about the distance of surfaces or objects in the scene from a particular viewpoint. Different viewpoints generate different depth maps. In their article, “Depth Map Generation by Image Classification,” published in the Proceedings of SPIE, Three-Dimensional Image Capture and Applications (VI 5302, pp. 95-104, 2004) Battiato, et al., generated the final depth map by fusion of a depth map from image classification and depth map from a vanishing point. But this method does not provide the robust depth for video and is not suitable for implementation in hardware.
In their article, “A Real-Time 2-D to 3-D Image Conversion Technique Using Computed Image Depth,” published in SID Digest of Technical Papers (32. 2 pp. 912-922, 1998), Murata, et al., propose an adaptive control between the Modified Time Difference (MTD) and the Computed Image Depth (CID) image. The MTD can take stereo-pair from the input image sequence but only handles simple horizontal motion. The CID calculates the depth of each part of the input 2D image with its contrast, sharpness, chrominance and composition. However, this method has issues on the transition between MTD and CID because it does not use interpolated images. This method also uses a simple motion model and cannot handle complex or vertical motion.